Overview

Dataset statistics

Number of variables28
Number of observations37799
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.1 MiB
Average record size in memory224.0 B

Variable types

Numeric23
Categorical4
Boolean1

Alerts

name has a high cardinality: 36688 distinct values High cardinality
host_name has a high cardinality: 6898 distinct values High cardinality
price is highly correlated with accommodatesHigh correlation
number_of_reviews is highly correlated with reviews_per_monthHigh correlation
reviews_per_month is highly correlated with number_of_reviews and 2 other fieldsHigh correlation
availability_365 is highly correlated with availability_60 and 1 other fieldsHigh correlation
number_of_reviews_ltm is highly correlated with reviews_per_month and 1 other fieldsHigh correlation
accommodates is highly correlated with price and 2 other fieldsHigh correlation
bedrooms is highly correlated with accommodates and 1 other fieldsHigh correlation
beds is highly correlated with accommodates and 1 other fieldsHigh correlation
availability_60 is highly correlated with availability_365 and 1 other fieldsHigh correlation
number_of_reviews_l30d is highly correlated with reviews_per_month and 1 other fieldsHigh correlation
availability_90 is highly correlated with availability_365 and 1 other fieldsHigh correlation
review_scores_accuracy is highly correlated with review_scores_cleanliness and 3 other fieldsHigh correlation
review_scores_cleanliness is highly correlated with review_scores_accuracy and 1 other fieldsHigh correlation
review_scores_checkin is highly correlated with review_scores_accuracy and 1 other fieldsHigh correlation
review_scores_communication is highly correlated with review_scores_accuracy and 2 other fieldsHigh correlation
review_scores_value is highly correlated with review_scores_accuracy and 2 other fieldsHigh correlation
id is highly correlated with host_idHigh correlation
host_id is highly correlated with idHigh correlation
price is highly correlated with accommodatesHigh correlation
number_of_reviews is highly correlated with reviews_per_month and 1 other fieldsHigh correlation
reviews_per_month is highly correlated with number_of_reviews and 2 other fieldsHigh correlation
availability_365 is highly correlated with availability_60 and 1 other fieldsHigh correlation
number_of_reviews_ltm is highly correlated with number_of_reviews and 2 other fieldsHigh correlation
accommodates is highly correlated with price and 2 other fieldsHigh correlation
bedrooms is highly correlated with accommodates and 1 other fieldsHigh correlation
beds is highly correlated with accommodates and 1 other fieldsHigh correlation
availability_60 is highly correlated with availability_365 and 1 other fieldsHigh correlation
number_of_reviews_l30d is highly correlated with reviews_per_month and 1 other fieldsHigh correlation
availability_90 is highly correlated with availability_365 and 1 other fieldsHigh correlation
review_scores_accuracy is highly correlated with review_scores_cleanliness and 4 other fieldsHigh correlation
review_scores_cleanliness is highly correlated with review_scores_accuracy and 3 other fieldsHigh correlation
review_scores_checkin is highly correlated with review_scores_accuracy and 3 other fieldsHigh correlation
review_scores_communication is highly correlated with review_scores_accuracy and 3 other fieldsHigh correlation
review_scores_location is highly correlated with review_scores_accuracy and 1 other fieldsHigh correlation
review_scores_value is highly correlated with review_scores_accuracy and 4 other fieldsHigh correlation
number_of_reviews is highly correlated with reviews_per_monthHigh correlation
reviews_per_month is highly correlated with number_of_reviews and 1 other fieldsHigh correlation
availability_365 is highly correlated with availability_60 and 1 other fieldsHigh correlation
number_of_reviews_ltm is highly correlated with reviews_per_month and 1 other fieldsHigh correlation
accommodates is highly correlated with bedrooms and 1 other fieldsHigh correlation
bedrooms is highly correlated with accommodates and 1 other fieldsHigh correlation
beds is highly correlated with accommodates and 1 other fieldsHigh correlation
availability_60 is highly correlated with availability_365 and 1 other fieldsHigh correlation
number_of_reviews_l30d is highly correlated with number_of_reviews_ltmHigh correlation
availability_90 is highly correlated with availability_365 and 1 other fieldsHigh correlation
review_scores_accuracy is highly correlated with review_scores_valueHigh correlation
review_scores_checkin is highly correlated with review_scores_communicationHigh correlation
review_scores_communication is highly correlated with review_scores_checkinHigh correlation
review_scores_value is highly correlated with review_scores_accuracyHigh correlation
id is highly correlated with host_idHigh correlation
host_id is highly correlated with idHigh correlation
neighbourhood is highly correlated with latitude and 1 other fieldsHigh correlation
latitude is highly correlated with neighbourhoodHigh correlation
longitude is highly correlated with neighbourhoodHigh correlation
price is highly correlated with accommodatesHigh correlation
number_of_reviews is highly correlated with reviews_per_month and 1 other fieldsHigh correlation
reviews_per_month is highly correlated with number_of_reviews and 2 other fieldsHigh correlation
availability_365 is highly correlated with availability_60 and 1 other fieldsHigh correlation
number_of_reviews_ltm is highly correlated with number_of_reviews and 2 other fieldsHigh correlation
accommodates is highly correlated with price and 2 other fieldsHigh correlation
bedrooms is highly correlated with accommodates and 1 other fieldsHigh correlation
beds is highly correlated with accommodates and 1 other fieldsHigh correlation
availability_60 is highly correlated with availability_365 and 1 other fieldsHigh correlation
number_of_reviews_l30d is highly correlated with reviews_per_month and 1 other fieldsHigh correlation
availability_90 is highly correlated with availability_365 and 1 other fieldsHigh correlation
review_scores_accuracy is highly correlated with review_scores_cleanliness and 4 other fieldsHigh correlation
review_scores_cleanliness is highly correlated with review_scores_accuracy and 4 other fieldsHigh correlation
review_scores_checkin is highly correlated with review_scores_accuracy and 4 other fieldsHigh correlation
review_scores_communication is highly correlated with review_scores_accuracy and 4 other fieldsHigh correlation
review_scores_location is highly correlated with review_scores_accuracy and 4 other fieldsHigh correlation
review_scores_value is highly correlated with review_scores_accuracy and 4 other fieldsHigh correlation
number_of_reviews_ltm is highly skewed (γ1 = 47.14442199) Skewed
beds is highly skewed (γ1 = 27.74387527) Skewed
name is uniformly distributed Uniform
id has unique values Unique
availability_365 has 17341 (45.9%) zeros Zeros
number_of_reviews_ltm has 19302 (51.1%) zeros Zeros
bedrooms has 7116 (18.8%) zeros Zeros
availability_60 has 19832 (52.5%) zeros Zeros
number_of_reviews_l30d has 28366 (75.0%) zeros Zeros
availability_90 has 19045 (50.4%) zeros Zeros

Reproduction

Analysis started2022-03-04 16:58:16.112610
Analysis finished2022-03-04 17:00:18.656660
Duration2 minutes and 2.54 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

id
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct37799
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25642764.47
Minimum5396
Maximum53669791
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size295.4 KiB
2022-03-04T18:00:18.753258image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum5396
5-th percentile2004151.5
Q112174246
median24804703
Q338925700
95-th percentile51259237
Maximum53669791
Range53664395
Interquartile range (IQR)26751454

Descriptive statistics

Standard deviation15753979.77
Coefficient of variation (CV)0.6143635483
Kurtosis-1.186988607
Mean25642764.47
Median Absolute Deviation (MAD)13368377
Skewness0.09947270955
Sum9.692708543 × 1011
Variance2.481878786 × 1014
MonotonicityStrictly increasing
2022-03-04T18:00:18.909083image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53961
 
< 0.1%
346706841
 
< 0.1%
346540601
 
< 0.1%
346568571
 
< 0.1%
346574371
 
< 0.1%
346587151
 
< 0.1%
346601081
 
< 0.1%
346611711
 
< 0.1%
346621231
 
< 0.1%
346627321
 
< 0.1%
Other values (37789)37789
> 99.9%
ValueCountFrequency (%)
53961
< 0.1%
73971
< 0.1%
79641
< 0.1%
99521
< 0.1%
105861
< 0.1%
105881
< 0.1%
109171
< 0.1%
112131
< 0.1%
112651
< 0.1%
114871
< 0.1%
ValueCountFrequency (%)
536697911
< 0.1%
536579651
< 0.1%
536406871
< 0.1%
536382441
< 0.1%
536173911
< 0.1%
536166681
< 0.1%
536002061
< 0.1%
535989411
< 0.1%
535904611
< 0.1%
535903361
< 0.1%

name
Categorical

HIGH CARDINALITY
UNIFORM

Distinct36688
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Memory size295.4 KiB
RARE - Gorgeous Apartment in the Haut-Marais!
 
17
Gorgeous studio in the Haut-Marais
 
14
Studio
 
12
 
12
Studio in the heart of Paris
 
11
Other values (36683)
37733 

Length

Max length255
Median length38
Mean length37.85549882
Min length1

Characters and Unicode

Total characters82
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique36006 ?
Unique (%)95.3%

Sample

1st rowExplore the heart of old Paris
2nd rowMARAIS - 2ROOMS APT - 2/4 PEOPLE
3rd rowLarge & sunny flat with balcony !
4th rowParis petit coin douillet
5th rowStudio 7 Montmartre

Common Values

ValueCountFrequency (%)
RARE - Gorgeous Apartment in the Haut-Marais!17
 
< 0.1%
Gorgeous studio in the Haut-Marais14
 
< 0.1%
Studio12
 
< 0.1%
12
 
< 0.1%
Studio in the heart of Paris11
 
< 0.1%
Gorgeous Apartment in the Haut-Marais!10
 
< 0.1%
Charmant studio au coeur de Paris10
 
< 0.1%
RARE - Gorgeous Studio close from NATION!10
 
< 0.1%
Charmant appartement parisien9
 
< 0.1%
Charming flat in the heart of Paris8
 
< 0.1%
Other values (36678)37686
99.7%

Length

2022-03-04T18:00:19.091338image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
10029
 
4.2%
paris9690
 
4.1%
in6949
 
2.9%
studio6619
 
2.8%
appartement5267
 
2.2%
de4826
 
2.0%
apartment4496
 
1.9%
the4148
 
1.8%
cosy3900
 
1.6%
flat3784
 
1.6%
Other values (10214)177044
74.8%

Most occurring characters

ValueCountFrequency (%)
82
100.0%

Most occurring categories

ValueCountFrequency (%)
Control82
100.0%

Most frequent character per category

Control
ValueCountFrequency (%)
82
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common82
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
82
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII82
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
82
100.0%

host_id
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct30420
Distinct (%)80.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93576745.23
Minimum2626
Maximum434633507
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size295.4 KiB
2022-03-04T18:00:19.255599image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2626
5-th percentile2385039.6
Q112465338
median37314388
Q3136004195
95-th percentile367952388.2
Maximum434633507
Range434630881
Interquartile range (IQR)123538857

Descriptive statistics

Standard deviation116018598
Coefficient of variation (CV)1.239822968
Kurtosis0.8902304204
Mean93576745.23
Median Absolute Deviation (MAD)31162678
Skewness1.436943757
Sum3.537107393 × 1012
Variance1.346031508 × 1016
MonotonicityNot monotonic
2022-03-04T18:00:19.414976image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
402191311205
 
0.5%
17037121157
 
0.4%
7642792148
 
0.4%
50978178133
 
0.4%
291007369114
 
0.3%
37358256194
 
0.2%
266737089
 
0.2%
2698105480
 
0.2%
210747877
 
0.2%
14701868575
 
0.2%
Other values (30410)36627
96.9%
ValueCountFrequency (%)
26262
< 0.1%
67924
< 0.1%
79031
 
< 0.1%
94121
 
< 0.1%
151461
 
< 0.1%
188761
 
< 0.1%
191051
 
< 0.1%
206331
 
< 0.1%
221551
 
< 0.1%
235591
 
< 0.1%
ValueCountFrequency (%)
4346335071
< 0.1%
4340582631
< 0.1%
4339883801
< 0.1%
4336591231
< 0.1%
4332585191
< 0.1%
4331221441
< 0.1%
4329173061
< 0.1%
4329032801
< 0.1%
4328432121
< 0.1%
4328425671
< 0.1%

host_name
Categorical

HIGH CARDINALITY

Distinct6898
Distinct (%)18.2%
Missing0
Missing (%)0.0%
Memory size295.4 KiB
Marie
 
390
Pierre
 
367
Guillaume
 
271
Antoine
 
270
Nicolas
 
259
Other values (6893)
36242 

Length

Max length30
Median length6
Mean length7.141194211
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4309 ?
Unique (%)11.4%

Sample

1st rowBorzou
2nd rowFranck
3rd rowAnaïs
4th rowElisabeth
5th rowMichael

Common Values

ValueCountFrequency (%)
Marie390
 
1.0%
Pierre367
 
1.0%
Guillaume271
 
0.7%
Antoine270
 
0.7%
Nicolas259
 
0.7%
Sophie258
 
0.7%
Checkmyguest242
 
0.6%
Camille233
 
0.6%
Thomas230
 
0.6%
Anne226
 
0.6%
Other values (6888)35053
92.7%

Length

2022-03-04T18:00:19.581273image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
marie391
 
1.0%
pierre367
 
1.0%
checkmyguest344
 
0.9%
guillaume271
 
0.7%
antoine270
 
0.7%
nicolas259
 
0.7%
sophie258
 
0.7%
camille233
 
0.6%
thomas230
 
0.6%
anne226
 
0.6%
Other values (6862)34950
92.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

neighbourhood
Categorical

HIGH CORRELATION

Distinct20
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size295.4 KiB
Buttes-Montmartre
4058 
Popincourt
3660 
Vaugirard
2847 
Entrepôt
2834 
Batignolles-Monceau
2259 
Other values (15)
22141 

Length

Max length19
Median length10
Mean length10.49104474
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHôtel-de-Ville
2nd rowHôtel-de-Ville
3rd rowOpéra
4th rowPopincourt
5th rowButtes-Montmartre

Common Values

ValueCountFrequency (%)
Buttes-Montmartre4058
 
10.7%
Popincourt3660
 
9.7%
Vaugirard2847
 
7.5%
Entrepôt2834
 
7.5%
Batignolles-Monceau2259
 
6.0%
Ménilmontant2112
 
5.6%
Buttes-Chaumont2103
 
5.6%
Opéra1973
 
5.2%
Temple1858
 
4.9%
Reuilly1625
 
4.3%
Other values (10)12470
33.0%

Length

2022-03-04T18:00:19.733837image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
buttes-montmartre4058
 
10.7%
popincourt3660
 
9.7%
vaugirard2847
 
7.5%
entrepôt2834
 
7.5%
batignolles-monceau2259
 
6.0%
ménilmontant2112
 
5.6%
buttes-chaumont2103
 
5.6%
opéra1973
 
5.2%
temple1858
 
4.9%
reuilly1625
 
4.3%
Other values (10)12470
33.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

latitude
Real number (ℝ≥0)

HIGH CORRELATION

Distinct7996
Distinct (%)21.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.86387301
Minimum48.81258
Maximum48.90486
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size295.4 KiB
2022-03-04T18:00:20.054457image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum48.81258
5-th percentile48.83225
Q148.85077
median48.86512
Q348.87831
95-th percentile48.8915
Maximum48.90486
Range0.09228
Interquartile range (IQR)0.02754

Descriptive statistics

Standard deviation0.01813527735
Coefficient of variation (CV)0.0003711387624
Kurtosis-0.7537098923
Mean48.86387301
Median Absolute Deviation (MAD)0.01378
Skewness-0.2239443537
Sum1847005.536
Variance0.0003288882847
MonotonicityNot monotonic
2022-03-04T18:00:20.208633image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48.8566333
 
0.1%
48.8535829
 
0.1%
48.863927
 
0.1%
48.8658521
 
0.1%
48.879321
 
0.1%
48.8588921
 
0.1%
48.8784521
 
0.1%
48.8686220
 
0.1%
48.8836920
 
0.1%
48.8639719
 
0.1%
Other values (7986)37567
99.4%
ValueCountFrequency (%)
48.812581
< 0.1%
48.8161
< 0.1%
48.816151
< 0.1%
48.816181
< 0.1%
48.816311
< 0.1%
48.816421
< 0.1%
48.816551
< 0.1%
48.816741
< 0.1%
48.816771
< 0.1%
48.81691
< 0.1%
ValueCountFrequency (%)
48.904861
< 0.1%
48.904111
< 0.1%
48.903891
< 0.1%
48.902331
< 0.1%
48.90171
< 0.1%
48.901591
< 0.1%
48.901581
< 0.1%
48.901391
< 0.1%
48.901231
< 0.1%
48.90121
< 0.1%

longitude
Real number (ℝ≥0)

HIGH CORRELATION

Distinct12752
Distinct (%)33.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.346090323
Minimum2.22929
Maximum2.47203
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size295.4 KiB
2022-03-04T18:00:20.359633image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2.22929
5-th percentile2.28686
Q12.32605
median2.34894
Q32.37029
95-th percentile2.39605
Maximum2.47203
Range0.24274
Interquartile range (IQR)0.04424

Descriptive statistics

Standard deviation0.03247806297
Coefficient of variation (CV)0.01384348362
Kurtosis-0.3401325767
Mean2.346090323
Median Absolute Deviation (MAD)0.02213
Skewness-0.3659102366
Sum88679.86813
Variance0.001054824574
MonotonicityNot monotonic
2022-03-04T18:00:20.536012image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.2852628
 
0.1%
2.336822
 
0.1%
2.3367418
 
< 0.1%
2.3567218
 
< 0.1%
2.3788818
 
< 0.1%
2.3469918
 
< 0.1%
2.3439918
 
< 0.1%
2.35818
 
< 0.1%
2.350217
 
< 0.1%
2.284617
 
< 0.1%
Other values (12742)37607
99.5%
ValueCountFrequency (%)
2.229291
< 0.1%
2.235491
< 0.1%
2.238571
< 0.1%
2.242731
< 0.1%
2.2470268851
< 0.1%
2.248041
< 0.1%
2.25071
< 0.1%
2.251621
< 0.1%
2.251731
< 0.1%
2.2518151
< 0.1%
ValueCountFrequency (%)
2.472031
< 0.1%
2.467121
< 0.1%
2.461131
< 0.1%
2.459651
< 0.1%
2.456991
< 0.1%
2.456471
< 0.1%
2.454991
< 0.1%
2.450411
< 0.1%
2.449881
< 0.1%
2.449741
< 0.1%

room_type
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size295.4 KiB
Entirehome/apt
31898 
Privateroom
4969 
Hotelroom
 
718
Sharedroom
 
214

Length

Max length14
Median length14
Mean length13.48800233
Min length9

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEntirehome/apt
2nd rowEntirehome/apt
3rd rowEntirehome/apt
4th rowEntirehome/apt
5th rowEntirehome/apt

Common Values

ValueCountFrequency (%)
Entirehome/apt31898
84.4%
Privateroom4969
 
13.1%
Hotelroom718
 
1.9%
Sharedroom214
 
0.6%

Length

2022-03-04T18:00:20.661845image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-04T18:00:20.737532image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
entirehome/apt31898
84.4%
privateroom4969
 
13.1%
hotelroom718
 
1.9%
sharedroom214
 
0.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

price
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct575
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean113.6163655
Minimum8
Maximum663
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size295.4 KiB
2022-03-04T18:00:20.840325image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile38
Q161
median90
Q3135
95-th percentile280
Maximum663
Range655
Interquartile range (IQR)74

Descriptive statistics

Standard deviation83.09689189
Coefficient of variation (CV)0.7313813597
Kurtosis8.116436879
Mean113.6163655
Median Absolute Deviation (MAD)31
Skewness2.461108861
Sum4294585
Variance6905.093442
MonotonicityNot monotonic
2022-03-04T18:00:20.980928image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
601494
 
4.0%
801466
 
3.9%
701418
 
3.8%
501342
 
3.6%
1001302
 
3.4%
901279
 
3.4%
651039
 
2.7%
120945
 
2.5%
75925
 
2.4%
55809
 
2.1%
Other values (565)25780
68.2%
ValueCountFrequency (%)
82
 
< 0.1%
91
 
< 0.1%
109
< 0.1%
112
 
< 0.1%
121
 
< 0.1%
142
 
< 0.1%
157
< 0.1%
164
 
< 0.1%
176
 
< 0.1%
1815
< 0.1%
ValueCountFrequency (%)
6631
 
< 0.1%
6571
 
< 0.1%
6551
 
< 0.1%
6522
 
< 0.1%
6509
< 0.1%
6483
 
< 0.1%
6471
 
< 0.1%
6463
 
< 0.1%
6441
 
< 0.1%
6434
< 0.1%

minimum_nights
Real number (ℝ≥0)

Distinct76
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean90.33836874
Minimum1
Maximum9999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size295.4 KiB
2022-03-04T18:00:21.128129image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q330
95-th percentile365
Maximum9999
Range9998
Interquartile range (IQR)28

Descriptive statistics

Standard deviation161.1835347
Coefficient of variation (CV)1.784220116
Kurtosis377.1159331
Mean90.33836874
Median Absolute Deviation (MAD)2
Skewness7.220135273
Sum3414700
Variance25980.13187
MonotonicityNot monotonic
2022-03-04T18:00:21.275244image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3658823
23.3%
27509
19.9%
16796
18.0%
35312
14.1%
42665
 
7.1%
52095
 
5.5%
301653
 
4.4%
7910
 
2.4%
6661
 
1.7%
10222
 
0.6%
Other values (66)1153
 
3.1%
ValueCountFrequency (%)
16796
18.0%
27509
19.9%
35312
14.1%
42665
 
7.1%
52095
 
5.5%
6661
 
1.7%
7910
 
2.4%
899
 
0.3%
944
 
0.1%
10222
 
0.6%
ValueCountFrequency (%)
99991
 
< 0.1%
11241
 
< 0.1%
10004
 
< 0.1%
9991
 
< 0.1%
5231
 
< 0.1%
5002
 
< 0.1%
4002
 
< 0.1%
3658823
23.3%
3602
 
< 0.1%
3251
 
< 0.1%

number_of_reviews
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct439
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.12016191
Minimum1
Maximum1809
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size295.4 KiB
2022-03-04T18:00:21.432021image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median11
Q330
95-th percentile115
Maximum1809
Range1808
Interquartile range (IQR)26

Descriptive statistics

Standard deviation51.18555469
Coefficient of variation (CV)1.820243954
Kurtosis74.84798347
Mean28.12016191
Median Absolute Deviation (MAD)9
Skewness5.713611171
Sum1062914
Variance2619.961009
MonotonicityNot monotonic
2022-03-04T18:00:21.583859image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14018
 
10.6%
22991
 
7.9%
32384
 
6.3%
41967
 
5.2%
51750
 
4.6%
61401
 
3.7%
71317
 
3.5%
81166
 
3.1%
91030
 
2.7%
10870
 
2.3%
Other values (429)18905
50.0%
ValueCountFrequency (%)
14018
10.6%
22991
7.9%
32384
6.3%
41967
5.2%
51750
4.6%
61401
 
3.7%
71317
 
3.5%
81166
 
3.1%
91030
 
2.7%
10870
 
2.3%
ValueCountFrequency (%)
18091
< 0.1%
11131
< 0.1%
8971
< 0.1%
8351
< 0.1%
8171
< 0.1%
8121
< 0.1%
7691
< 0.1%
6921
< 0.1%
6901
< 0.1%
6731
< 0.1%

reviews_per_month
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct761
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8121849784
Minimum0.01
Maximum50.86
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size295.4 KiB
2022-03-04T18:00:21.748224image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.03
Q10.13
median0.38
Q31
95-th percentile3.02
Maximum50.86
Range50.85
Interquartile range (IQR)0.87

Descriptive statistics

Standard deviation1.20124471
Coefficient of variation (CV)1.479028474
Kurtosis115.5093011
Mean0.8121849784
Median Absolute Deviation (MAD)0.31
Skewness5.904290067
Sum30699.78
Variance1.442988852
MonotonicityNot monotonic
2022-03-04T18:00:21.899065image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.041125
 
3.0%
0.031017
 
2.7%
0.07879
 
2.3%
0.06812
 
2.1%
0.05795
 
2.1%
0.02789
 
2.1%
0.08715
 
1.9%
0.09686
 
1.8%
0.12617
 
1.6%
0.1612
 
1.6%
Other values (751)29752
78.7%
ValueCountFrequency (%)
0.01595
1.6%
0.02789
2.1%
0.031017
2.7%
0.041125
3.0%
0.05795
2.1%
0.06812
2.1%
0.07879
2.3%
0.08715
1.9%
0.09686
1.8%
0.1612
1.6%
ValueCountFrequency (%)
50.861
< 0.1%
24.21
< 0.1%
22.741
< 0.1%
22.111
< 0.1%
21.791
< 0.1%
21.411
< 0.1%
21.321
< 0.1%
21.021
< 0.1%
19.91
< 0.1%
19.391
< 0.1%

calculated_host_listings_count
Real number (ℝ≥0)

Distinct64
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.456546469
Minimum1
Maximum252
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size295.4 KiB
2022-03-04T18:00:22.054294image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile52
Maximum252
Range251
Interquartile range (IQR)1

Descriptive statistics

Standard deviation32.89766435
Coefficient of variation (CV)3.478824374
Kurtosis29.77296253
Mean9.456546469
Median Absolute Deviation (MAD)0
Skewness5.29212722
Sum357448
Variance1082.25632
MonotonicityNot monotonic
2022-03-04T18:00:22.204522image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127683
73.2%
22930
 
7.8%
3906
 
2.4%
4548
 
1.4%
5470
 
1.2%
6368
 
1.0%
9250
 
0.7%
7244
 
0.6%
8224
 
0.6%
10213
 
0.6%
Other values (54)3963
 
10.5%
ValueCountFrequency (%)
127683
73.2%
22930
 
7.8%
3906
 
2.4%
4548
 
1.4%
5470
 
1.2%
6368
 
1.0%
7244
 
0.6%
8224
 
0.6%
9250
 
0.7%
10213
 
0.6%
ValueCountFrequency (%)
252205
0.5%
22089
0.2%
20673
 
0.2%
196148
0.4%
195157
0.4%
141114
0.3%
135133
0.4%
11213
 
< 0.1%
11111
 
< 0.1%
9994
0.2%

availability_365
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct366
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.0242334
Minimum0
Maximum365
Zeros17341
Zeros (%)45.9%
Negative0
Negative (%)0.0%
Memory size295.4 KiB
2022-03-04T18:00:22.352846image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median6
Q3201
95-th percentile355
Maximum365
Range365
Interquartile range (IQR)201

Descriptive statistics

Standard deviation132.4187233
Coefficient of variation (CV)1.323866415
Kurtosis-0.7716180521
Mean100.0242334
Median Absolute Deviation (MAD)6
Skewness0.9328589163
Sum3780816
Variance17534.71829
MonotonicityNot monotonic
2022-03-04T18:00:22.496916image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
017341
45.9%
1423
 
1.1%
365394
 
1.0%
364331
 
0.9%
2291
 
0.8%
3253
 
0.7%
4237
 
0.6%
8222
 
0.6%
363208
 
0.6%
342203
 
0.5%
Other values (356)17896
47.3%
ValueCountFrequency (%)
017341
45.9%
1423
 
1.1%
2291
 
0.8%
3253
 
0.7%
4237
 
0.6%
5194
 
0.5%
6169
 
0.4%
7162
 
0.4%
8222
 
0.6%
9122
 
0.3%
ValueCountFrequency (%)
365394
1.0%
364331
0.9%
363208
0.6%
362179
0.5%
36193
 
0.2%
360100
 
0.3%
359108
 
0.3%
358133
 
0.4%
357108
 
0.3%
356132
 
0.3%

number_of_reviews_ltm
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct141
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.734569698
Minimum0
Maximum1705
Zeros19302
Zeros (%)51.1%
Negative0
Negative (%)0.0%
Memory size295.4 KiB
2022-03-04T18:00:22.649299image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q35
95-th percentile22
Maximum1705
Range1705
Interquartile range (IQR)5

Descriptive statistics

Standard deviation14.72378984
Coefficient of variation (CV)3.109847522
Kurtosis4858.56997
Mean4.734569698
Median Absolute Deviation (MAD)0
Skewness47.14442199
Sum178962
Variance216.7899872
MonotonicityNot monotonic
2022-03-04T18:00:22.795175image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
019302
51.1%
13361
 
8.9%
22219
 
5.9%
31686
 
4.5%
41383
 
3.7%
51157
 
3.1%
6938
 
2.5%
7827
 
2.2%
8699
 
1.8%
9592
 
1.6%
Other values (131)5635
 
14.9%
ValueCountFrequency (%)
019302
51.1%
13361
 
8.9%
22219
 
5.9%
31686
 
4.5%
41383
 
3.7%
51157
 
3.1%
6938
 
2.5%
7827
 
2.2%
8699
 
1.8%
9592
 
1.6%
ValueCountFrequency (%)
17051
< 0.1%
6271
< 0.1%
4101
< 0.1%
3951
< 0.1%
3631
< 0.1%
3211
< 0.1%
3051
< 0.1%
2801
< 0.1%
2551
< 0.1%
2461
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size37.0 KiB
False
31345 
True
6454 
ValueCountFrequency (%)
False31345
82.9%
True6454
 
17.1%
2022-03-04T18:00:22.997968image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

accommodates
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.038784095
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size295.4 KiB
2022-03-04T18:00:23.200645image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q34
95-th percentile6
Maximum16
Range15
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.569686977
Coefficient of variation (CV)0.5165510045
Kurtosis5.219155299
Mean3.038784095
Median Absolute Deviation (MAD)1
Skewness1.716565049
Sum114863
Variance2.463917205
MonotonicityNot monotonic
2022-03-04T18:00:23.324686image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
218643
49.3%
49108
24.1%
33489
 
9.2%
62209
 
5.8%
11934
 
5.1%
51420
 
3.8%
8480
 
1.3%
7268
 
0.7%
10117
 
0.3%
948
 
0.1%
Other values (6)83
 
0.2%
ValueCountFrequency (%)
11934
 
5.1%
218643
49.3%
33489
 
9.2%
49108
24.1%
51420
 
3.8%
62209
 
5.8%
7268
 
0.7%
8480
 
1.3%
948
 
0.1%
10117
 
0.3%
ValueCountFrequency (%)
1614
 
< 0.1%
153
 
< 0.1%
149
 
< 0.1%
133
 
< 0.1%
1244
 
0.1%
1110
 
< 0.1%
10117
 
0.3%
948
 
0.1%
8480
1.3%
7268
0.7%

bedrooms
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.099023784
Minimum0
Maximum50
Zeros7116
Zeros (%)18.8%
Negative0
Negative (%)0.0%
Memory size295.4 KiB
2022-03-04T18:00:23.436970image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile3
Maximum50
Range50
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.052768718
Coefficient of variation (CV)0.9579125892
Kurtosis883.4749599
Mean1.099023784
Median Absolute Deviation (MAD)0
Skewness19.78632399
Sum41542
Variance1.108321974
MonotonicityNot monotonic
2022-03-04T18:00:23.541351image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
122925
60.6%
07116
 
18.8%
25586
 
14.8%
31687
 
4.5%
4400
 
1.1%
564
 
0.2%
610
 
< 0.1%
507
 
< 0.1%
73
 
< 0.1%
331
 
< 0.1%
ValueCountFrequency (%)
07116
 
18.8%
122925
60.6%
25586
 
14.8%
31687
 
4.5%
4400
 
1.1%
564
 
0.2%
610
 
< 0.1%
73
 
< 0.1%
331
 
< 0.1%
507
 
< 0.1%
ValueCountFrequency (%)
507
 
< 0.1%
331
 
< 0.1%
73
 
< 0.1%
610
 
< 0.1%
564
 
0.2%
4400
 
1.1%
31687
 
4.5%
25586
 
14.8%
122925
60.6%
07116
 
18.8%

beds
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.696896743
Minimum1
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size295.4 KiB
2022-03-04T18:00:23.805304image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile4
Maximum90
Range89
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.385031303
Coefficient of variation (CV)0.81621425
Kurtosis1586.491474
Mean1.696896743
Median Absolute Deviation (MAD)0
Skewness27.74387527
Sum64141
Variance1.918311709
MonotonicityNot monotonic
2022-03-04T18:00:24.051795image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
121272
56.3%
210809
28.6%
33472
 
9.2%
41375
 
3.6%
5523
 
1.4%
6212
 
0.6%
768
 
0.2%
839
 
0.1%
916
 
< 0.1%
102
 
< 0.1%
Other values (8)11
 
< 0.1%
ValueCountFrequency (%)
121272
56.3%
210809
28.6%
33472
 
9.2%
41375
 
3.6%
5523
 
1.4%
6212
 
0.6%
768
 
0.2%
839
 
0.1%
916
 
< 0.1%
102
 
< 0.1%
ValueCountFrequency (%)
901
 
< 0.1%
851
 
< 0.1%
831
 
< 0.1%
791
 
< 0.1%
771
 
< 0.1%
182
 
< 0.1%
122
 
< 0.1%
112
 
< 0.1%
102
 
< 0.1%
916
< 0.1%

availability_60
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct61
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.4090849
Minimum0
Maximum60
Zeros19832
Zeros (%)52.5%
Negative0
Negative (%)0.0%
Memory size295.4 KiB
2022-03-04T18:00:24.354058image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q326
95-th percentile57
Maximum60
Range60
Interquartile range (IQR)26

Descriptive statistics

Standard deviation19.40500565
Coefficient of variation (CV)1.447153612
Kurtosis-0.04618689783
Mean13.4090849
Median Absolute Deviation (MAD)0
Skewness1.193783608
Sum506850
Variance376.5542441
MonotonicityNot monotonic
2022-03-04T18:00:24.809974image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
019832
52.5%
1881
 
2.3%
59696
 
1.8%
60652
 
1.7%
6606
 
1.6%
2566
 
1.5%
3511
 
1.4%
4485
 
1.3%
37462
 
1.2%
7441
 
1.2%
Other values (51)12667
33.5%
ValueCountFrequency (%)
019832
52.5%
1881
 
2.3%
2566
 
1.5%
3511
 
1.4%
4485
 
1.3%
5441
 
1.2%
6606
 
1.6%
7441
 
1.2%
8344
 
0.9%
9301
 
0.8%
ValueCountFrequency (%)
60652
1.7%
59696
1.8%
58436
1.2%
57337
0.9%
56147
 
0.4%
55176
 
0.5%
54162
 
0.4%
53283
0.7%
52195
 
0.5%
51211
 
0.6%

number_of_reviews_l30d
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct28
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5966295405
Minimum0
Maximum62
Zeros28366
Zeros (%)75.0%
Negative0
Negative (%)0.0%
Memory size295.4 KiB
2022-03-04T18:00:25.053198image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum62
Range62
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.564416701
Coefficient of variation (CV)2.622090585
Kurtosis268.7654253
Mean0.5966295405
Median Absolute Deviation (MAD)0
Skewness10.06574408
Sum22552
Variance2.447399614
MonotonicityNot monotonic
2022-03-04T18:00:25.172927image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
028366
75.0%
14015
 
10.6%
22311
 
6.1%
31358
 
3.6%
4744
 
2.0%
5470
 
1.2%
6218
 
0.6%
7123
 
0.3%
870
 
0.2%
940
 
0.1%
Other values (18)84
 
0.2%
ValueCountFrequency (%)
028366
75.0%
14015
 
10.6%
22311
 
6.1%
31358
 
3.6%
4744
 
2.0%
5470
 
1.2%
6218
 
0.6%
7123
 
0.3%
870
 
0.2%
940
 
0.1%
ValueCountFrequency (%)
623
< 0.1%
472
< 0.1%
411
 
< 0.1%
331
 
< 0.1%
252
< 0.1%
243
< 0.1%
233
< 0.1%
223
< 0.1%
201
 
< 0.1%
192
< 0.1%

availability_90
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct91
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.97478769
Minimum0
Maximum90
Zeros19045
Zeros (%)50.4%
Negative0
Negative (%)0.0%
Memory size295.4 KiB
2022-03-04T18:00:25.318138image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q348
95-th percentile87
Maximum90
Range90
Interquartile range (IQR)48

Descriptive statistics

Standard deviation31.26678944
Coefficient of variation (CV)1.360917448
Kurtosis-0.6463966708
Mean22.97478769
Median Absolute Deviation (MAD)0
Skewness0.9782731388
Sum868424
Variance977.6121216
MonotonicityNot monotonic
2022-03-04T18:00:25.463453image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
019045
50.4%
1685
 
1.8%
89676
 
1.8%
90638
 
1.7%
2467
 
1.2%
88431
 
1.1%
67408
 
1.1%
8406
 
1.1%
3380
 
1.0%
4370
 
1.0%
Other values (81)14293
37.8%
ValueCountFrequency (%)
019045
50.4%
1685
 
1.8%
2467
 
1.2%
3380
 
1.0%
4370
 
1.0%
5299
 
0.8%
6269
 
0.7%
7290
 
0.8%
8406
 
1.1%
9254
 
0.7%
ValueCountFrequency (%)
90638
1.7%
89676
1.8%
88431
1.1%
87336
0.9%
86143
 
0.4%
85177
 
0.5%
84146
 
0.4%
83271
0.7%
82182
 
0.5%
81196
 
0.5%

review_scores_accuracy
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct155
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.761118019
Minimum0
Maximum5
Zeros15
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size295.4 KiB
2022-03-04T18:00:25.609839image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q14.7
median4.88
Q35
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.4202306919
Coefficient of variation (CV)0.08826302776
Kurtosis34.49021276
Mean4.761118019
Median Absolute Deviation (MAD)0.12
Skewness-4.870955428
Sum179965.5
Variance0.1765938344
MonotonicityNot monotonic
2022-03-04T18:00:25.763026image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
512746
33.7%
4.51136
 
3.0%
4.671078
 
2.9%
41061
 
2.8%
4.75985
 
2.6%
4.88940
 
2.5%
4.83856
 
2.3%
4.8852
 
2.3%
4.92795
 
2.1%
4.86793
 
2.1%
Other values (145)16557
43.8%
ValueCountFrequency (%)
015
 
< 0.1%
1128
0.3%
1.331
 
< 0.1%
1.55
 
< 0.1%
1.671
 
< 0.1%
284
0.2%
2.21
 
< 0.1%
2.251
 
< 0.1%
2.338
 
< 0.1%
2.381
 
< 0.1%
ValueCountFrequency (%)
512746
33.7%
4.9972
 
0.2%
4.98212
 
0.6%
4.97360
 
1.0%
4.96480
 
1.3%
4.95625
 
1.7%
4.94652
 
1.7%
4.93708
 
1.9%
4.92795
 
2.1%
4.91657
 
1.7%

review_scores_cleanliness
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct210
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.596069473
Minimum0
Maximum5
Zeros15
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size295.4 KiB
2022-03-04T18:00:25.909936image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.67
Q14.46
median4.75
Q34.96
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.530732211
Coefficient of variation (CV)0.1154752369
Kurtosis14.57378675
Mean4.596069473
Median Absolute Deviation (MAD)0.25
Skewness-3.085343036
Sum173726.83
Variance0.2816766798
MonotonicityNot monotonic
2022-03-04T18:00:26.058378image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
58994
23.8%
41859
 
4.9%
4.51589
 
4.2%
4.671256
 
3.3%
4.751003
 
2.7%
4.8761
 
2.0%
4.33653
 
1.7%
4.86650
 
1.7%
4.83647
 
1.7%
4.88624
 
1.7%
Other values (200)19763
52.3%
ValueCountFrequency (%)
015
 
< 0.1%
1179
0.5%
1.511
 
< 0.1%
1.61
 
< 0.1%
1.674
 
< 0.1%
1.81
 
< 0.1%
2139
0.4%
2.22
 
< 0.1%
2.254
 
< 0.1%
2.291
 
< 0.1%
ValueCountFrequency (%)
58994
23.8%
4.9952
 
0.1%
4.98148
 
0.4%
4.97205
 
0.5%
4.96279
 
0.7%
4.95372
 
1.0%
4.94371
 
1.0%
4.93408
 
1.1%
4.92531
 
1.4%
4.91426
 
1.1%

review_scores_checkin
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct164
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.803414905
Minimum0
Maximum5
Zeros17
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size295.4 KiB
2022-03-04T18:00:26.206782image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.2
Q14.77
median4.93
Q35
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)0.23

Descriptive statistics

Standard deviation0.3958847787
Coefficient of variation (CV)0.08241736068
Kurtosis43.35925125
Mean4.803414905
Median Absolute Deviation (MAD)0.07
Skewness-5.491581202
Sum181564.28
Variance0.156724758
MonotonicityNot monotonic
2022-03-04T18:00:26.359084image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
515080
39.9%
4.67877
 
2.3%
4.5849
 
2.2%
4.83842
 
2.2%
4.88829
 
2.2%
4.92828
 
2.2%
4795
 
2.1%
4.75794
 
2.1%
4.94794
 
2.1%
4.93788
 
2.1%
Other values (154)15323
40.5%
ValueCountFrequency (%)
017
 
< 0.1%
1123
0.3%
1.51
 
< 0.1%
257
0.2%
2.21
 
< 0.1%
2.251
 
< 0.1%
2.333
 
< 0.1%
2.513
 
< 0.1%
2.62
 
< 0.1%
2.631
 
< 0.1%
ValueCountFrequency (%)
515080
39.9%
4.99127
 
0.3%
4.98356
 
0.9%
4.97530
 
1.4%
4.96660
 
1.7%
4.95726
 
1.9%
4.94794
 
2.1%
4.93788
 
2.1%
4.92828
 
2.2%
4.91750
 
2.0%

review_scores_communication
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct159
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.814449853
Minimum0
Maximum5
Zeros12
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size295.4 KiB
2022-03-04T18:00:26.510235image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.2
Q14.79
median4.95
Q35
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)0.21

Descriptive statistics

Standard deviation0.3979532246
Coefficient of variation (CV)0.08265808904
Kurtosis43.4325092
Mean4.814449853
Median Absolute Deviation (MAD)0.05
Skewness-5.583369954
Sum181981.39
Variance0.158366769
MonotonicityNot monotonic
2022-03-04T18:00:26.683763image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
516194
42.8%
4.92870
 
2.3%
4.88829
 
2.2%
4.94797
 
2.1%
4.5795
 
2.1%
4.95793
 
2.1%
4.67773
 
2.0%
4.93765
 
2.0%
4762
 
2.0%
4.83757
 
2.0%
Other values (149)14464
38.3%
ValueCountFrequency (%)
012
 
< 0.1%
1134
0.4%
1.53
 
< 0.1%
1.61
 
< 0.1%
1.671
 
< 0.1%
262
0.2%
2.252
 
< 0.1%
2.338
 
< 0.1%
2.43
 
< 0.1%
2.516
 
< 0.1%
ValueCountFrequency (%)
516194
42.8%
4.99182
 
0.5%
4.98475
 
1.3%
4.97626
 
1.7%
4.96675
 
1.8%
4.95793
 
2.1%
4.94797
 
2.1%
4.93765
 
2.0%
4.92870
 
2.3%
4.91662
 
1.8%

review_scores_location
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct153
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.805943543
Minimum0
Maximum5
Zeros15
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size295.4 KiB
2022-03-04T18:00:26.836125image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.25
Q14.75
median4.92
Q35
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)0.25

Descriptive statistics

Standard deviation0.3477388246
Coefficient of variation (CV)0.07235599451
Kurtosis47.12302358
Mean4.805943543
Median Absolute Deviation (MAD)0.08
Skewness-5.33906022
Sum181659.86
Variance0.1209222902
MonotonicityNot monotonic
2022-03-04T18:00:27.020139image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
514173
37.5%
4.51097
 
2.9%
4.671016
 
2.7%
4948
 
2.5%
4.75934
 
2.5%
4.88902
 
2.4%
4.8862
 
2.3%
4.92830
 
2.2%
4.83824
 
2.2%
4.86812
 
2.1%
Other values (143)15401
40.7%
ValueCountFrequency (%)
015
 
< 0.1%
168
0.2%
1.52
 
< 0.1%
228
0.1%
2.55
 
< 0.1%
2.671
 
< 0.1%
2.712
 
< 0.1%
2.752
 
< 0.1%
2.831
 
< 0.1%
2.91
 
< 0.1%
ValueCountFrequency (%)
514173
37.5%
4.99176
 
0.5%
4.98398
 
1.1%
4.97550
 
1.5%
4.96631
 
1.7%
4.95728
 
1.9%
4.94737
 
1.9%
4.93755
 
2.0%
4.92830
 
2.2%
4.91702
 
1.9%

review_scores_value
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct176
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.618767957
Minimum0
Maximum5
Zeros13
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size295.4 KiB
2022-03-04T18:00:27.176358image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q14.5
median4.72
Q34.9
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.4632611382
Coefficient of variation (CV)0.1002997212
Kurtosis21.42891684
Mean4.618767957
Median Absolute Deviation (MAD)0.21
Skewness-3.650778462
Sum174584.81
Variance0.2146108822
MonotonicityNot monotonic
2022-03-04T18:00:27.324243image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
57891
20.9%
4.51902
 
5.0%
41773
 
4.7%
4.671543
 
4.1%
4.751288
 
3.4%
4.8961
 
2.5%
4.83793
 
2.1%
4.71711
 
1.9%
4.33690
 
1.8%
4.6684
 
1.8%
Other values (166)19563
51.8%
ValueCountFrequency (%)
013
 
< 0.1%
1149
0.4%
1.331
 
< 0.1%
1.56
 
< 0.1%
1.81
 
< 0.1%
296
0.3%
2.252
 
< 0.1%
2.338
 
< 0.1%
2.43
 
< 0.1%
2.533
 
0.1%
ValueCountFrequency (%)
57891
20.9%
4.993
 
< 0.1%
4.9812
 
< 0.1%
4.9737
 
0.1%
4.9678
 
0.2%
4.95128
 
0.3%
4.94163
 
0.4%
4.93212
 
0.6%
4.92327
 
0.9%
4.91272
 
0.7%

Interactions

2022-03-04T18:00:11.165721image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:58:27.027116image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:58:31.146918image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:58:35.899977image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:58:40.591980image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:58:45.667558image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:58:50.444459image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:59:01.051870image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:59:09.226673image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:59:14.803752image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:59:19.956271image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:59:24.217994image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:59:28.542420image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:59:33.523266image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:59:38.278239image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:59:42.236775image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:59:45.469040image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:59:48.812329image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:59:52.192779image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:59:57.185849image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T18:00:00.805433image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T18:00:04.514561image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T18:00:07.857186image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T18:00:11.296200image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:58:27.215038image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:58:31.354621image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:58:36.079372image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:58:40.756088image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:58:45.818212image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:58:50.653226image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:59:01.225178image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:59:09.450921image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:59:15.050404image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:59:20.106762image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:59:24.369615image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:59:28.782714image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:59:33.681910image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:59:38.418146image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:59:42.374028image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:59:45.655885image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:59:48.943818image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:59:52.331352image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:59:57.440452image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-03-04T17:58:59.736030image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:59:08.407997image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:59:13.330624image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:59:19.339551image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:59:23.481940image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:59:27.899759image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:59:32.407350image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:59:37.016702image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:59:41.443711image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:59:44.916193image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:59:48.261769image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:59:51.649138image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:59:56.341654image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T18:00:00.261849image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T18:00:03.965402image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T18:00:07.130701image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T18:00:10.613377image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T18:00:15.270946image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:58:30.204141image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:58:35.275830image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:58:40.005961image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:58:45.185453image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:58:49.699675image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:58:59.982911image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:59:08.692928image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:59:13.914529image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:59:19.502826image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:59:23.783893image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:59:28.047579image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:59:32.852172image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:59:37.748118image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:59:41.774776image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:59:45.054603image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:59:48.402847image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:59:51.794756image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:59:56.701817image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T18:00:00.396310image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T18:00:04.110210image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T18:00:07.263447image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T18:00:10.753364image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T18:00:15.417180image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:58:30.457874image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:58:35.433320image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:58:40.202724image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:58:45.367162image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:58:50.056258image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:59:00.356267image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:59:08.880778image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:59:14.143979image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:59:19.651506image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:59:23.932785image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:59:28.201917image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:59:33.049901image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:59:38.007173image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:59:41.954612image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:59:45.191998image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:59:48.540214image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:59:51.932059image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:59:56.886289image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T18:00:00.527783image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T18:00:04.246013image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T18:00:07.396621image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T18:00:10.889306image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T18:00:15.558970image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:58:30.808132image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:58:35.603589image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:58:40.387578image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:58:45.521762image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:58:50.278074image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:59:00.808206image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:59:09.052039image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:59:14.463001image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:59:19.798512image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:59:24.075810image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:59:28.357814image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:59:33.206010image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:59:38.147076image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:59:42.100122image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:59:45.334326image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:59:48.675069image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:59:52.062514image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T17:59:57.051832image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T18:00:00.656834image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T18:00:04.381464image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T18:00:07.538553image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T18:00:11.027401image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-03-04T18:00:27.482558image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-03-04T18:00:27.786779image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-03-04T18:00:28.117013image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-03-04T18:00:28.362279image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-03-04T18:00:28.506444image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-03-04T18:00:16.471015image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-03-04T18:00:18.254443image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

idnamehost_idhost_nameneighbourhoodlatitudelongituderoom_typepriceminimum_nightsnumber_of_reviewsreviews_per_monthcalculated_host_listings_countavailability_365number_of_reviews_ltmhost_is_superhostaccommodatesbedroomsbedsavailability_60number_of_reviews_l30davailability_90review_scores_accuracyreview_scores_cleanlinessreview_scores_checkinreview_scores_communicationreview_scores_locationreview_scores_value
05396Explore the heart of old Paris7903BorzouHôtel-de-Ville48.8524702.358350Entirehome/apt10222731.8015842f20.01.0283584.564.484.774.814.964.53
17397MARAIS - 2ROOMS APT - 2/4 PEOPLE2626FranckHôtel-de-Ville48.8590902.353150Entirehome/apt112102882.22220919t42.02.052244.794.434.914.884.924.71
27964Large & sunny flat with balcony !22155AnaïsOpéra48.8741702.342450Entirehome/apt130660.0413440f21.01.0390695.005.005.005.005.005.00
39952Paris petit coin douillet33534ElisabethPopincourt48.8637302.370930Entirehome/apt814330.3112607t21.01.030254.974.885.004.914.914.94
410586Studio 7 Montmartre37107MichaelButtes-Montmartre48.8870002.345310Entirehome/apt8030490.3441621t20.02.0290294.774.774.884.984.604.67
510588Studio 10 Montmartre37107MichaelButtes-Montmartre48.8872502.345180Entirehome/apt7530190.1542173t20.01.0100104.884.945.004.944.594.69
610917ELYSEES-PONCELET FLAT NEAR CH. ELYS39402IsabelleBatignolles-Monceau48.8790742.296904Entirehome/apt14330250.17100t41.02.00004.504.194.313.934.404.06
711213DOWNTOWN PARIS41322MathieuEntrepôt48.8710902.373760Privateroom17011511.9322524f61.03.00004.774.364.864.924.694.55
811265Elegant appartment in Montmartre41718SylvieButtes-Montmartre48.8849402.339970Entirehome/apt1007160.24100f21.01.00004.814.944.534.754.934.80
911487Heart of Paris, brand new aparment.42666BrigittePopincourt48.8644102.371390Entirehome/apt603040.0312162t20.01.0220224.254.754.754.754.754.25

Last rows

idnamehost_idhost_nameneighbourhoodlatitudelongituderoom_typepriceminimum_nightsnumber_of_reviewsreviews_per_monthcalculated_host_listings_countavailability_365number_of_reviews_ltmhost_is_superhostaccommodatesbedroomsbedsavailability_60number_of_reviews_l30davailability_90review_scores_accuracyreview_scores_cleanlinessreview_scores_checkinreview_scores_communicationreview_scores_locationreview_scores_value
3778953590336Appartement Cosy proche Montmartre66695202AnnalisaButtes-Montmartre48.8936512.346627Entirehome/apt90311.013611f21.01.0561865.05.05.05.05.05.0
3779053590461Appartement Cosy en plein coeur de Paris433988380LoubnaReuilly48.8385772.390153Entirehome/apt48311.013241f20.01.0191495.04.05.05.05.05.0
3779153598941SUPERB studio with BALCONY in the HEART OF PARIS434058263HenriMénilmontant48.8687592.399991Entirehome/apt62111.0131f20.01.03135.05.05.05.05.05.0
3779253600206Superbe appartement dans le 5ème160134472RitaPanthéon48.8428272.350496Entirehome/apt250111.03481f42.02.0101105.05.04.05.05.04.0
3779353616668Studio design avec vue sur la Tour Eiffel21238185EmilieVaugirard48.8436972.284839Entirehome/apt100111.0171t21.01.07175.05.05.05.05.05.0
3779453617391wide studio with a balcony and a convertible sofa5345079PruneGobelins48.8279602.368460Entirehome/apt39111.011331f21.01.02125.05.05.05.05.05.0
3779553638244Maison Boissiere | Deluxe apartment Arc de Triomphe51567288SweetInnPassy48.8681142.289649Entirehome/apt312211.0522581f41.02.0521825.05.05.05.05.05.0
3779653640687Female only! Femme seulement.46032712CheerPalais-Bourbon48.8580252.328207Sharedroom60111.011751f11.01.0551855.05.05.05.05.05.0
3779753657965Artist Atelier with garden - Tour Eiffel430505007ValentinVaugirard48.8493302.288450Entirehome/apt200111.033531f61.03.0481785.05.05.05.05.05.0
3779853669791Lovely studio walking distance from Champs-Élysées434633507ChristyÉlysée48.8737072.308950Entirehome/apt100222.01202f20.01.0202205.05.05.05.05.05.0